DETAILED ACTION
Comments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1, 3-4, 8-9, 11, 13, 15-18, 22, and 24 are pending and examined in the instant Office action.
Withdrawn Rejections
The first 35 U.S.C. 101 rejection from the prior Office action is withdrawn in view of amendments filed to the instant set of claims on 18 February 2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The following rejection is reiterated:
Claim(s) 1, 4, 9, 11, 13, 15-16, 18, 22, and 24 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. Claims 1, 4, 9, 11, 13, and 15 are drawn to methods, and claims 16, 18, 22, and 24 are drawn to systems comprising processors.
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1 : YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
The independent claims recite the mental step of receiving desired properties of the molecule.
The independent claims recite the mental step of generating candidate molecules using a first machine learning technique that uses desired properties of the molecule as the input.
The independent claims recite the mental step of computing routes to synthesize the candidate molecule using a second machine learning technique.
The independent claims recite the mental step of constraining the second machine learning technique to using data relating to precursor molecules or reactions.
The independent claims recite the mental step of outputting a representation of the at least one molecule and one or more associated routes to synthesis.
The independent claims recite the mental step of using a reactor treed for the candidate molecule to precursor molecules using a tree search method.
The independent claims recite the mental step of requiring the reaction tree to select and expand nodes of the reaction tree by using a machine learning model trained to recognize valid chemical reactions.
The independent claims recite the mental step of requiring the feedback to be based on user input.
Claims 4 and 18 recite the mental step of ranking the candidate molecules based on one of the one or more desired properties.
Claim 9 recites the mental steps of selecting a promising precursor node and generating a random sequence of valid reactions terminating in a node which represents a precursor that is known or for which no precursors are available, generating a coarse prediction of a value of further expanding the node.
Claim 9 recites the mental step of backpropagating the prediction to the root node to update the reaction tree.
Claim 11 recites the mental steps of providing feedback from the machine learning techniques regarding success and failure.
Claims 13 and 22 recite the mental step of storing the computed routes.
Claims 15 and 24 recite the mental step of constraining the one or more desired properties.
These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the “Mental processes” grouping of abstract ideas. As such, claim(s) 1, 4, 9, 11, 13, 15-16, 18, 22, and 24 recite(s) an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 1 : YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception to affect a particular treatment for a condition. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or mere instructions to apply the recited judicial exception via a generic treatment.
There are no limitations that indicate that the claimed analysis engine or the formats of the provided data require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. As such, claims 1, 4, 9, 11, 13, 15-16, 18, 22, and 24 is/are directed to an abstract idea/law of nature/natural phenomenon (Step 2A, Prong 2 : NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
As discussed above, there are no additional limitations to indicate that the claimed analysis engine requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B : No). As such, claims 1, 4, 9, 11, 13, 15-16, 18, 22, and 24 is/are not patent eligible.
Claims 3, 8, and 17 are NOT rejection in this 35 U.S.C. 101 rejection because the claims recite machine learning techniques and Monte Carlo algorithms that are too complex to be performed in the human mind.
Response to arguments:
Applicant's arguments filed 18 February 2026 have been fully considered but they are not persuasive.
Applicant argues that the amendments to the claims overcome the rejection. However, the amendments to the claims incorporate the mental steps of prior dependent claims (that are now cancelled) into independent claims. Applicant argues that the amendments to the claims result in algorithms that are more advanced (i.e. with multiple types of machine learning in the same algorithm with an incorporated feedback mechanism) than conventional algorithms with analogous objectives. This argument is not persuasive because a judicial exception that is an improvement to a judicial exception remains a judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following rejection is reiterated:
Claim(s) 1, 3-4, 8-9, 11, 13, 15-18, 22, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Segler et al. [Nature, volume 555, March 2018, pages 604-610; on IDS] in view of Vamathevan et al. [Nature Reviews Drug Discovery, volume 18, 11 April 2019, pages 463-477; on IDS].
Claim 1 is drawn to a method for designing a molecule for use as a potential drug candidate and determining a route to synthesize the molecule. The method comprises receiving one or more desired properties of the molecule. The method comprises generating representations of one or more candidate molecules using a first machine learning technique that uses the one or more desired properties of the molecule as input. The method comprises, that for each candidate molecule, computing one or more routes to synthesize the candidate molecule using a second machine learning technique. The technique uses the representations of the candidate molecule and data relating to precursor molecules and reactions as inputs. The computing one or more routes to synthesize each candidate molecule comprises exploring a reaction tree from the candidate molecule to precursor molecules using a tree search method. The technique involves selecting and expanding nodes of the reaction tree by using a machine learning model trained to recognize valid chemical reactions. The method comprises outputting representations of candidate molecules and one or more associated routes to synthesis. The method comprises automatically providing feedback to the first machine learning technique indicating a suitability of one of the computed routes to synthesis in order to change a likelihood of future outputs of the first machine learning model.
Claim 16 is drawn to similar subject matter as claim 1, except claim 16 is drawn to a system.
The document of Segler et al. studies planning chemical syntheses with deep neural networks and symbolic AI [title]. Figure 5 on page 608 of Segler et al. compares a machine learning derived technique for chemical synthesis with a published technique in prior scientific literature. Figure 5 on page 608 of Segler et al. teaches that the machine learning pertains to precursor molecules and reactions. Figure 5 on page 608 of Segler et al. teaches representing one molecule and associated synthesis routes. Figure 2 on page 606 of Segler et al. teaches a reaction tree for precursor molecules with expanding nodes based on machine learning. The abstract of Segler et al. teaches a Monte Carlo tree search. Figure 5 on page 608 of Segler et al. uses preference ratios to suggest differences between successful literature derived synthesis techniques with machine learning derived synthesis techniques. The feedback of Figure 5 of Segler et al. is based upon the user input molecule.
Segler et al. does not teach using a different machine learning technique to derive the initial molecule based on input properties.
The document of Vamathevan et al. studies applications of machine learning in drug discovery and development [title]. The abstract of Vamathevan et al. teaches deriving drug candidates. Figure 1 on page 464 of Vamathevan et al. teaches obtaining molecules with desirable properties using a different machine learning technique than the machine learning taught in Segler et al.
With regard to claims 3 and 17, Figure 2 on page 465 of Vamathevan et al. teaches used of RNNs and general adversarial networks.
With regard to claims 4 and 18, Figure 4 on page 471 of Vamathevan et al. suggests ranking candidate molecules based on patient survival.
With regard to claim 8, Figure 2 on page 606 of Segler et al. teaches a reaction tree for precursor molecules with expanding nodes based on machine learning. The abstract of Segler et al. teaches a Monte Carlo tree search wherein the trees have leaves and nodes.
With regard to claim 9, absent a description of what constitutes a random sequence or reactions, the sequence of precursors and reactions in Figure 5 of Segler et al. is interpreted to be a random sequence of reactions. The abstract of Segler et al. teaches a recursive algorithm that backpropagates the reaction sequence.
With regard to claim 11, Figure 5 on page 608 of Segler et al. uses preference ratios to suggest differences between successful literature derived synthesis techniques with machine learning derived synthesis techniques. The feedback of Figure 5 of Segler et al. is based upon the user input molecule.
With regard to claims 13 and 22, the intended use of the macro “for use in a further synthesis route computation using the second machine learning technique” does not differentiate the claim from the prior art. Figure 5 of Segler et al. stories the computer synthesis routes.
With regard to claims 15 and 24, the first paragraph of column 1 on page 468 of Vamathevan et al. teaches studying drug toxicity.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the use of machine learning to derive a synthesis route of a molecule as in Segler et al. by use of machine learning to identify the molecule to be synthesized based on properties of the molecule as in Vamathevan et al. wherein the motivation would have been that the machine learning of Vamathevan et al. facilitates the machine learning of Segler et al. by deriving the initial molecule to be synthesized [Figure 1 on page 464 of Vamathevan et al.]. There would have been a reasonable success in combining Segler et al. an Vamathevan et al. because both studies are analogously applicable to applying machine learning to small organic molecules.
Response to arguments:
Applicant's arguments filed 18 February 2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art does not teach a plurality of machine learning techniques. This argument is not persuasive because the combination of Segler et al. and Vamathevan et al. teaches a plurality of machine learning techniques.
Applicant argues that Figure 5 of Segler et al. does not teach feedback to the machine learning model. Absent a limiting description of the term “feedback” in the specification, the prevalence ration in Figures 5a and 5b of Segler et al. are broadly construed to comprise relevant feedback of the AI preference for a reaction sequence. Even though the document of Segler et al. may not use the exact term “feedback,” this interpretation of Figures 5a and 5b of Segler et al. encompass the broadly construed meaning of “feedback.”
Related Prior Art
The prior art of Segler et al. [arXiv, 14 August 2017; on IDS] is a similar study to that of Segler et al. cited in the rejection statement in that this second Segler et al. document also uses machine learning and tree search methods to plan chemical syntheses.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/Primary Examiner, Art Unit 1686 30 July 2026
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